Abstract
The problem of choosing the most plausible attachment for a prepositional phrase (PP) or a relative clause (RC) is one of the most significant challenges to a NL Processing system, since it presupposes an in-depth understanding of the interaction between the different sources of knowledge involved in the task of processing a sentence (in particular, we consider syntactic and semantic knowledge). In the paper, we argue that traditional categorical approaches do not adequately face the intrinsic complexity of the attachment problem and we describe a more flexible approach, based on management of uncertainty: both syntax and semantics provide a Confidence Degree (CD) for the linguistic connections, and a weighed combination of these CDs allow the system to select the most plausible connection when different alternatives are possible.
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Terenziani, P., Lesmo, L., Gerbino, E. (1991). Management of uncertainty in the attachment problem in natural language processing. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Uncertainty in Knowledge Bases. IPMU 1990. Lecture Notes in Computer Science, vol 521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028133
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DOI: https://doi.org/10.1007/BFb0028133
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